Spectral reflectance of whale skin above the sea surface: a proposed measurement protocol

Great whales have been detected using very-high-resolution satellite imagery, suggesting this technology could be used to monitor whales in remote areas. However, the application of this method to whale studies is at an early developmental stage and several technical factors need to be addressed, including capacity for species differentiation and the maximum depth of detection in the water column. Both require knowledge of the spectral reflectance of the various whale species just above the sea surface, as when whales bodies break the surface of the water to breath, log or breach, there is, theoretically, no sea water between the whale’s skin and the satellite sensor. Here we tested whether such reflectance could be measured on dead whale tissue. We measured the spectral reflectance of fresh integument collected during the bowhead subsistence harvest, and of thawed integument samples from various species obtained following strandings and stored at -20°C. We show that fresh and thawed samples of whale integument have different spectral properties. The reflectance of fresh samples was higher than the reflectance of thawed samples, as integument appears to darken after death and with time, even under frozen conditions. In this study, we present the first whale reflectance estimates (without the influence of sea water and for dead tissue). These provide a baseline for additional work, needed to advance the use of satellite imagery to monitor whales and facilitate their conservation.MAVA Foundation, B B Roberts Fund, Cambridge Philosophical Society, Prescott Stranding Grants

Comparative study of Selva and Camarosa strawberries for the commercial market

Selva and Camarosa strawberry varieties were characterized chemically and physically. The importance of keeping the stem until processing, the influence of different transport periods under refrigerated conditions, the effects of freezing and exposure to air of damaged surfaces were evaluated. During freezing, losses of ascorbic acid, sucrose, fructose and glucose were reported for both varieties. However, keeping the stem intact minimizes the losses of ascorbic acid in frozen fruits. The exposure to air of cut surfaces affects ascorbic acid content of fresh fruits, with the highest losses reported in Camarosa. Selva showed properties important for commercial use, as compared to Camarosa, with regard to a higher resistance to thawing and higher contents of total phenolics, total protein, and ascorbic acid

An administrative data merging solution for dealing with missing data in a clinical registry: adaptation from ICD-9 to ICD-10

<p>Abstract</p> <p>Background</p> <p>We have previously described a method for dealing with missing data in a prospective cardiac registry initiative. The method involves merging registry data to corresponding ICD-9-CM administrative data to fill in missing data 'holes'. Here, we describe the process of translating our data merging solution to ICD-10, and then validating its performance.</p> <p>Methods</p> <p>A multi-step translation process was undertaken to produce an ICD-10 algorithm, and merging was then implemented to produce complete datasets for 1995–2001 based on the ICD-9-CM coding algorithm, and for 2002–2005 based on the ICD-10 algorithm. We used cardiac registry data for patients undergoing cardiac catheterization in fiscal years 1995–2005. The corresponding administrative data records were coded in ICD-9-CM for 1995–2001 and in ICD-10 for 2002–2005. The resulting datasets were then evaluated for their ability to predict death at one year.</p> <p>Results</p> <p>The prevalence of the individual clinical risk factors increased gradually across years. There was, however, no evidence of either an abrupt drop or rise in prevalence of any of the risk factors. The performance of the new data merging model was comparable to that of our previously reported methodology: c-statistic = 0.788 (95% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 (95% CI 0.780, 0.790) for the ICD-9-CM model. The two models also exhibited similar goodness-of-fit.</p> <p>Conclusion</p> <p>The ICD-10 implementation of our data merging method performs as well as the previously-validated ICD-9-CM method. Such methodological research is an essential prerequisite for research with administrative data now that most health systems are transitioning to ICD-10.</p

Being Ready to Treat Ebola Virus Disease Patients

An unprecedented number of health care professionals from a variety of clinical settings, in a wide range of countries are thinking about, preparing for and caring for Ebola virus disease (EVD) patients. Guidance documents on infection prevention and control (IPC) practice and clinical care have been produced by organizations with EVD experience.1–3 The World Health Organization (WHO) produces guidance for implementation across a wide range of resource settings. Medecin Sans Frontières produces guidance for medical team activities across the outbreak. The Centers for Disease Control and Prevention (CDC) focus on measures which can be taken by the United States health system and extrapolated by others involved in preparedness and response. There are no short cuts to clinical preparedness for EVD. These documents and their revisions should be reviewed carefully. As important as guidance documents are, many lessons must be learned from specific hands-on experience. The WHO has mobilized clinical consultants in support of EVD response in each of the affected countries in West Africa. This short list of key points attempts to consolidate practical lessons learned that do not always percolate into technical documents. Having landed in unconstrained, resource-limited settings at the start of local EVD clinical operations in an outbreak, and more established EVD care centers, we hope that others might adopt some of these lessons and avoid some of the risks inherent to the steep learning curve associated with delivering EVD care. The points are geared toward the daily care of patients as opposed to the critical mechanics of establishing a care center and developing its procedures. They are focused on the outbreak setting and also have relevance to the referral hospital setting

Relapse prevention for addictive behaviors

The Relapse Prevention (RP) model has been a mainstay of addictions theory and treatment since its introduction three decades ago. This paper provides an overview and update of RP for addictive behaviors with a focus on developments over the last decade (2000-2010). Major treatment outcome studies and meta-analyses are summarized, as are selected empirical findings relevant to the tenets of the RP model. Notable advances in RP in the last decade include the introduction of a reformulated cognitive-behavioral model of relapse, the application of advanced statistical methods to model relapse in large randomized trials, and the development of mindfulness-based relapse prevention. We also review the emergent literature on genetic correlates of relapse following pharmacological and behavioral treatments. The continued influence of RP is evidenced by its integration in most cognitive-behavioral substance use interventions. However, the tendency to subsume RP within other treatment modalities has posed a barrier to systematic evaluation of the RP model. Overall, RP remains an influential cognitive-behavioral framework that can inform both theoretical and clinical approaches to understanding and facilitating behavior change

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Causes of severe pneumonia requiring hospital admission in children without HIV infection from Africa and Asia: the PERCH multi-country case-control study

Background Pneumonia is the leading cause of death among children younger than 5 years. In this study, we estimated causes of pneumonia in young African and Asian children, using novel analytical methods applied to clinical and microbiological findings. Methods We did a multi-site, international case-control study in nine study sites in seven countries: Bangladesh, The Gambia, Kenya, Mali, South Africa, Thailand, and Zambia. All sites enrolled in the study for 24 months. Cases were children aged 1–59 months admitted to hospital with severe pneumonia. Controls were age-group-matched children randomly selected from communities surrounding study sites. Nasopharyngeal and oropharyngeal (NP-OP), urine, blood, induced sputum, lung aspirate, pleural fluid, and gastric aspirates were tested with cultures, multiplex PCR, or both. Primary analyses were restricted to cases without HIV infection and with abnormal chest x-rays and to controls without HIV infection. We applied a Bayesian, partial latent class analysis to estimate probabilities of aetiological agents at the individual and population level, incorporating case and control data. Findings Between Aug 15, 2011, and Jan 30, 2014, we enrolled 4232 cases and 5119 community controls. The primary analysis group was comprised of 1769 (41·8% of 4232) cases without HIV infection and with positive chest x-rays and 5102 (99·7% of 5119) community controls without HIV infection. Wheezing was present in 555 (31·7%) of 1752 cases (range by site 10·6–97·3%). 30-day case-fatality ratio was 6·4% (114 of 1769 cases). Blood cultures were positive in 56 (3·2%) of 1749 cases, and Streptococcus pneumoniae was the most common bacteria isolated (19 [33·9%] of 56). Almost all cases (98·9%) and controls (98·0%) had at least one pathogen detected by PCR in the NP-OP specimen. The detection of respiratory syncytial virus (RSV), parainfluenza virus, human metapneumovirus, influenza virus, S pneumoniae, Haemophilus influenzae type b (Hib), H influenzae non-type b, and Pneumocystis jirovecii in NP-OP specimens was associated with case status. The aetiology analysis estimated that viruses accounted for 61·4% (95% credible interval [CrI] 57·3–65·6) of causes, whereas bacteria accounted for 27·3% (23·3–31·6) and Mycobacterium tuberculosis for 5·9% (3·9–8·3). Viruses were less common (54·5%, 95% CrI 47·4–61·5 vs 68·0%, 62·7–72·7) and bacteria more common (33·7%, 27·2–40·8 vs 22·8%, 18·3–27·6) in very severe pneumonia cases than in severe cases. RSV had the greatest aetiological fraction (31·1%, 95% CrI 28·4–34·2) of all pathogens. Human rhinovirus, human metapneumovirus A or B, human parainfluenza virus, S pneumoniae, M tuberculosis, and H influenzae each accounted for 5% or more of the aetiological distribution. We observed differences in aetiological fraction by age for Bordetella pertussis, parainfluenza types 1 and 3, parechovirus–enterovirus, P jirovecii, RSV, rhinovirus, Staphylococcus aureus, and S pneumoniae, and differences by severity for RSV, S aureus, S pneumoniae, and parainfluenza type 3. The leading ten pathogens of each site accounted for 79% or more of the site's aetiological fraction. Interpretation In our study, a small set of pathogens accounted for most cases of pneumonia requiring hospital admission. Preventing and treating a subset of pathogens could substantially affect childhood pneumonia outcomes